Fertilization stability is an important index for evaluating the operational performance of variable fertilizer dischargers. To study the influence law of the combination of fertilizer discharge wheel rotational speed n and opening L on the fertilizer discharge performance, this paper firstly constructs a fertilizer amount prediction model based on a radial basis function neural network (RBFNN) through a calibration test, and after verification, its determination coefficient reaches 0.99965 with a mean relative error (MRE) of 3.88%. Then the discrete element simulation software (EDEM) was used to simulate the fertilizer discharge process under different control sequences for each of the three target fertilizer application amounts. The simulation results show that at the target fertilizer discharge rate of 944.92 g/min, when the control sequence is 18.3 r/min and 25 mm, the uniformity coefficient of variation (CV) of fertilizer discharge is the smallest. In the other control sequences, σ was higher than 20%, the stability of fertilizer discharge was poor, and the phenomenon of broken strips appeared; under the target fertilizer discharge rate of 2101.47 g/min, σ was the smallest at (24.2 r/min, 45 mm) 4.34%; under the target fertilizer discharge rate of 3842.87 g/min, σ was less than 4% in all cases, and at the control sequence (44.7 r/min, 45 mm), σ reached a minimum of 2.01%. Finally, using the simulation results and the prediction model of fertilizer amount based on RBFNN, the optimization model of fertilizer discharge control sequence based on the differential evolutionary (DE) algorithm was established, and a bench test was conducted to verify the optimization results, which showed that the accuracy and uniformity of fertilizer discharge met the operational requirements.

A PV maximum power tracking strategy for shaded conditions, based on an improved slime mold algorithm, is proposed in this research. To verify the superiority of the proposed algorithm, four bionomics algorithms—particle swarm optimization (PSO), tuna swarm optimization (TSO), squirrel search algorithm (SSA), and black widow spider algorithm (BWO)—were compared. The output parameter of the five control algorithms was summarized and analyzed. The adaptability of the algorithms was proven by setting different shading conditions. The simulation results demonstrated that the proposed algorithm possessed short response time, good tracking effect and fewer fluctuations. Eventually, the different algorithms were verified in the HIL + RCP physical platform. The experimental outcomes showed that the improved slime mold algorithm possessed the best tracking effect, with fewer power fluctuations.

The feeder, a critical component of pneumatic conveying and distributing fertilizer machines for cotton, significantly impacts cotton quality and yield. This study addresses the limitations of conventional feeders, namely poor delivery stability and insufficient air–fertilizer mixture uniformity, by introducing a novel feeder design incorporating guide-flow chamber vanes. Based on the structural principles of pneumatic fertilizer distribution and the theory of vortex flow feeding, the new feeder design was developed and analyzed using a CFD-DEM coupled model to simulate its operational process. Response surface methodology was employed to optimize key structural parameters, aiming to enhance both air–fertilizer mixture uniformity and fertilizer delivery stability. The optimized design features two guide vanes, a fertilizer discharge contraction angle of 23.2°, and eight chamber lines. Validation through simulation confirmed the model’s reliability, with a discrepancy of less than 5% between simulated and predicted values. Experimental validation on a pneumatic fertilizing test prototype further demonstrated the effectiveness of the optimization, resulting in a feeder stability coefficient of variation of 13.30% and a coefficient of variation in fertilizer distribution across the system’s branches of 3.96%, satisfying practical operational requirements. After optimization, the stability and uniformity of the feeder fertilizer are significantly improved. The optimized feeder design effectively improves the stability and air–fertilizer mixing uniformity of cotton pneumatic fertilizing machines, providing valuable theoretical and technical support for their design optimization and performance enhancement.